U.S. patent application number 16/090172 was filed with the patent office on 2019-04-18 for three-dimensional representation of skin structure.
The applicant listed for this patent is AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH, NATIONAL SKIN CENTRE (SINGAPORE) PTE LTD. Invention is credited to Jun CHENG, Annan LI, Jiang LIU, Ruchir SRIVASTAVA, Hong Liang TEY, Carolin WALL, Wing Kee Damon WONG, Ai Ping YOW.
Application Number | 20190110739 16/090172 |
Document ID | / |
Family ID | 59965029 |
Filed Date | 2019-04-18 |
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United States Patent
Application |
20190110739 |
Kind Code |
A1 |
TEY; Hong Liang ; et
al. |
April 18, 2019 |
THREE-DIMENSIONAL REPRESENTATION OF SKIN STRUCTURE
Abstract
The present disclosure generally relates to an automated method
and system for generating a three-dimensional (3D) representation
of a skin structure of a subject. The method comprises: acquiring a
plurality of two-dimensional (2D) cross-sectional images of the
skin structure, specifically, using optical coherence tomography
(OCT) technique; computing a cost for each 2D cross-sectional image
based on a cost function, the cost function comprising an
edge-based parameter and a non-edge-based parameter; constructing a
3D graph from the 2D cross-sectional images; and determining a
minimum-cost closed set from the 3D graph based on the computed
costs for the 2D cross-sectional images, wherein the 3D
representation of the skin structure is generated from the
minimum-cost closed set.
Inventors: |
TEY; Hong Liang; (Singapore,
SG) ; SRIVASTAVA; Ruchir; (Singapore, SG) ;
YOW; Ai Ping; (Singapore, SG) ; CHENG; Jun;
(Singapore, SG) ; LI; Annan; (Singapore, SG)
; WONG; Wing Kee Damon; (Singapore, SG) ; LIU;
Jiang; (Singapore, SG) ; WALL; Carolin;
(Singapore, SG) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AGENCY FOR SCIENCE, TECHNOLOGY AND RESEARCH
NATIONAL SKIN CENTRE (SINGAPORE) PTE LTD |
Singapore
Singapore |
|
SG
SG |
|
|
Family ID: |
59965029 |
Appl. No.: |
16/090172 |
Filed: |
March 28, 2017 |
PCT Filed: |
March 28, 2017 |
PCT NO: |
PCT/SG2017/050166 |
371 Date: |
September 28, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 7/0012 20130101;
G06T 7/12 20170101; G06T 2207/10101 20130101; A61B 5/442 20130101;
A61B 2576/02 20130101; G06T 2207/30088 20130101; G06T 7/162
20170101; A61B 5/0066 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; G06T 7/162 20060101 G06T007/162; G06T 7/12 20060101
G06T007/12; G06T 7/00 20060101 G06T007/00 |
Foreign Application Data
Date |
Code |
Application Number |
Mar 28, 2016 |
SG |
10201602395U |
Claims
1. An automated method for generating a three-dimensional (3D)
representation of a skin structure of a subject, the method
comprising: acquiring a plurality of two-dimensional (2D)
cross-sectional images of the skin structure; computing a cost for
each 2D cross-sectional image based on a cost function, the cost
function comprising an edge-based parameter and a non-edge-based
parameter; constructing a 3D graph from the 2D cross-sectional
images; and determining a minimum-cost closed set from the 3D graph
based on the computed costs for the 2D cross-sectional images,
wherein the 3D representation of the skin structure is generated
from the minimum-cost closed set.
2. The method according to claim 1, wherein the 2D cross-sectional
images comprise a plurality of nodes forming the 3D graph.
3. The method according to claim 2, wherein the 3D representation
comprises a plurality of voxels, each voxel corresponding to one of
the nodes.
4. The method according to claim 2, wherein computing the costs for
the 2D cross-sectional images comprises computing a cost for each
node.
5. (canceled)
6. The method according to claim 1, wherein the edge-based
parameter is associated with gradient information.
7-8. (canceled)
9. The method according to claim 1, further comprising performing
skin topographic analysis on the 3D representation to assess skin
roughness of the subject.
10. The method according to claim 9, wherein the skin topographic
analysis comprises performing a plane rectification process.
11. The method according to claim 10, wherein the skin topographic
analysis further comprises generating a 2D depth map.
12. The method according to claim 11, wherein the skin topographic
analysis further comprises computing a set of roughness
parameters.
13. The method according to claim 12, wherein the roughness
parameters are calculated based on a sliding window approach on the
2D depth map.
14. The method according to claim 12, wherein the set of roughness
parameters comprises amplitude and frequency parameters.
15. A system for generating a three-dimensional (3D) representation
of a skin structure of a subject, the system comprising a processor
configured for performing operations comprising: acquiring a
plurality of two-dimensional (2D) cross-sectional images of the
skin structure; computing a cost for each 2D cross-sectional image
based on a cost function, the cost function comprising an
edge-based parameter and a non-edge-based parameter; constructing a
3D graph from the 2D cross-sectional images; and determining a
minimum-cost closed set from the 3D graph based on the computed
costs for the 2D cross-sectional images, wherein the 3D
representation of the skin structure is generated from the
minimum-cost closed set.
16. The system according to claim 15, wherein the 2D
cross-sectional images comprise a plurality of nodes forming the 3D
graph.
17. The system according to claim 16, wherein the 3D representation
comprises a plurality of voxels, each voxel corresponding to one of
the nodes.
18. The system according to claim 16, wherein computing the costs
for the 2D cross-sectional images comprises computing a cost for
each node.
19. The system according to claim 15, wherein each 2D
cross-sectional image is a 2D optical coherence tomography (OCT)
image.
20. The system according to claim 15, wherein the edge-based
parameter is associated with gradient information.
21. The system according to claim 15, wherein the non-edge-based
parameter is associated with homogeneity information.
22. The system according to claim 21, wherein the non-edge-based
parameter is associated with a measure of a dark to bright
transition.
23. The system according to claim 15, the operations further
comprising performing a skin topographic analysis on the 3D
representation to assess skin roughness of the subject.
24-28. (canceled)
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present disclosure claims the benefit of Singapore
Patent Application No. 10201602395U filed on 28 Mar. 2016, which is
incorporated in its entirety by reference herein.
TECHNICAL FIELD
[0002] The present disclosure generally relates to
three-dimensional (3D) representation of skin structure. More
particularly, the present disclosure describes various embodiments
of an automated method and system for generating a 3D
representation of the skin structure of a subject (e.g. human
patient or candidate).
BACKGROUND
References
[0003] [1] Lev que, J. L. (1999), EEMCO guidance for the assessment
of skin topography. Journal of the European Academy of Dermatology
and Venereology, 12: 103-114. doi:
10.1111/j.1468-3083.1999.tb00998.x [0004] [2] Takanori Igarashi, Ko
Nishino, Shree K. Nayar, The Appearance of Human Skin: A Survey,
Foundations and Trends.RTM. in Computer Graphics and Vision, v. 3
n. 1, p. 1-95, January 2007 [0005] [3] J. Weissman, T. Hancewicz,
and P. Kaplan. Optical coherence tomography of skin for measurement
of epidermal thickness by shapelet-based image analysis. Optics
express, 12(23):5760-5769, 2004. [0006] [4] Y. Hori, Y. Yasuno, S.
Sakai, M. Matsumoto, T. Sugawara, V. Madjarova, M. Yamanari, S.
Makita, T. Yasui, T. Araki, et al. Automatic characterization and
segmentation of human skin using three-dimensional optical
coherence tomography. optics express, 14(5):1862-1877, 2006. [0007]
[5] A. Mcheik, C. Tauber, H. Batatia, J. George, and J.-M. Lagarde.
Speckle modelization in OCT images for skin layers segmentation. In
VISAPP (1), pages 347-350, 2008. [0008] [6] A. Li, J. Cheng, A. P.
Yow, C. Wall, D. Wong, H. Tey, and J. Liu. Epidermal segmentation
in high-definition optical coherence tomography. In IEEE EMBC,
2015. [0009] [7] A. P. Yow, J. Cheng, A. Li, C. Wall, D. Wong J.
Liu and H. Tey, Skin Surface Topographic Assessment using in vivo
High-Definition Optical Coherence Tomography. In 17th International
Conference on Information and Computer Security, 2015. [0010] [8]
Lioudmila Tchvialeva, Haishan Zeng, Igor Markhvida, David I McLean,
Harvey Lui and Tim K Lee (2010). Skin Roughness Assessment, New
Developments in Biomedical Engineering, Domenico Campolo (Ed.),
ISBN: 978-953-7619-57-2, InTech [0011] [9] Boone, Marc A L M, et
al. High-definition optical coherence tomography 15 imaging of
melanocytic lesions: a pilot study. Archives of dermatological
research (2013): 1-16. [0012] [10] Y. Boykov and V. Kolmogorov. An
experimental comparison of min-cut/max-flow algorithms for energy
minimization in vision. Pattern Analysis and Machine Intelligence,
IEEE Transactions on, 26(9):1124-1137, 2004.
[0013] The skin is the outer covering of a human body and is the
largest organ of the integumentary system. Skin characteristics can
vary due to numerous factors [References 1, 2] such as malignancy,
ageing, cosmetics, and personal care products. Hence, the
evaluation of the human skin structure and surface topography is of
particular importance to dermatological, cosmetic, pharmaceutical,
and/or cosmeceutical practice and research.
[0014] Optical coherence tomography (OCT) has emerged as a useful
non-invasive imaging modality for medical image analysis, including
for human skin structure and surface. OCT provides a higher
resolution than ultrasound and deeper penetration than confocal
microscopy images, and provides an ideal balance between
penetration depth and resolution. OCT images are manually examined
by dermatologists/clinicians for detecting abnormalities in the
skin or assessing response of skin to treatments or and/or
cosmetics products. However, manual examination is burdensome and
subjective. Computerized analysis of OCT images can be used as a
screening tool to assist dermatologists/clinicians. Such screening
can filter out the obvious healthy cases and refer the more serious
or less than-healthy cases to the dermatologists/clinicians.
[0015] One of the major challenges in analyzing OCT images of the
skin is the presence of body hair. This is especially relevant when
assessing hairy areas of the body (such as the face, outer aspects
of the forearms and calves, chest, and the back of the hands) or in
imaging scalp diseases. Hair obstructs imaging of the skin
resulting in poor contrast in some parts of the skin structure as
shown in FIG. 1A (hairs are indicated by arrows).
[0016] For computerized screening in dermatological applications,
the skin structure is segmented to identify the different layers of
the skin structure, because there are certain skin conditions which
are observed in specific layers. The major layers of the skin
structure are stratum-corneum, epidermis, and dermis. Epidermal
thickness measurement is important for assessing skin health,
ageing and photodamage. Measurement of the epidermal thickness is
possible by segmenting the dermoepidermal junction (DEJ) which is
the boundary between epidermis and dermis. DEJ segmentation depends
on accurate segmentation of skin structure since the external skin
surface provides a constraint for epidermis segmentation.
[0017] Skin structure segmentation is thus performed as a part of
measuring the epidermal thickness. Conventional techniques use
shapelets [Reference 3] to segment the skin structure or use
intensity values [Reference 4] for the segmentation. While some
techniques [Reference 5] attempt to characterize layers of the skin
structure using speckle noise distribution but do not really
segment the skin structure.
[0018] Other existing methods [Reference 6] employ a graph-based
method for skin surface segmentation whereby vertical intensity
gradients are used in the cost function. In another existing
graph-based method [Reference 7], vertical intensity gradients and
gradient magnitude are used in the cost function. These methods
have not considered the presence of body hair which can cause
significant errors in profiling the skin structure and surface as
shown in FIG. 1B. Current clinical practice is to remove hair by
shaving. However, shaving causes skin flakes which can induce
inaccuracies in skin surface topography analysis.
[0019] Irregularities in skin surface topography constitute
roughness and wrinkles observable by the human eye. Skin surface
roughness and wrinkles is an important evaluation item in evidence
reports on the progress of dermatological treatment. In most
clinical settings, skin surface roughness and wrinkles are
primarily assessed by visual inspection, with a critical dependence
on dermatologists'/clinicians' experiences. Besides visual
inspection, roughness and wrinkles can also be quantitatively
measured indirectly from skin replicas [Reference 8] which are
produced by imprinting the skin surface onto a silicone material.
Roughness measurement is then performed on the skin replicas using
a mechanical profilometry approach or optical techniques such as
microphotography. However, replica-based methods are inconvenient
in clinical settings and susceptible to distortions (including the
presence of air bubbles) during skin replica reproduction, and
require long scanning times [Reference 1].
[0020] Direct in vivo methods may be employed for analyzing the
skin surface topography. One existing method used for in vivo skin
analysis is fringe projection area topography [Reference 3].
Examples of such area topography systems include PRIMOS.RTM. and
DermaTOP.RTM.. Despite having a fast acquisition time of less than
1 second, the drawbacks of such fringe projection methods are the
interference of back scattering from skin tissue volume effects,
the deformation of fringe image caused by micro body movement, and
the low accuracy due to a moderate resolution of 15 to 24 .mu.m
[Reference 3].
[0021] More recently, high-definition OCT (HD-OCT) is available
with an enhanced resolution of 3 .mu.m in both axial and en face
planes, allowing for better visualization of the skin structure
[Reference 9] and a relatively fast image acquisition time of 2 to
3 seconds. With its high resolution and fast acquisition, HD-OCT
can be used as a potential tool for precise analysis of the skin
surface topography, particularly to assess irregularities in the
skin surface topography.
[0022] As described above, one of the main challenges in analyzing
skin surface topography is the presence of body hair on the skin
surface. The presence of hair imaged during image acquisition may
appear as bright blobs floating above the skin surface on the OCT
images. This results in a shadow cast upon the skin surface,
weakening the contrast of the edge at the skin surface, as shown in
FIG. 2A (indicated by arrows). The strong edges of the hairs may
also attract the path edges of the skin surface and lead to false
detection of the skin surface boundary as shown in FIG. 2B. The
upper image in FIG. 2B illustrates the presence of hair as blobs
(indicated by arrows), and the lower image in FIG. 2B illustrates
the false skin surface boundary due to the presence of hair.
[0023] Therefore, in order to address or alleviate at least one of
the aforementioned problems and/or disadvantages, there is a need
to provide an automated method and system for generating a 3D
representation of a skin structure of a subject, in which there is
at least one improvement and/or advantage over the aforementioned
prior art.
SUMMARY
[0024] According to an aspect of the present disclosure, there is
an automated method and system for generating a three-dimensional
(3D) representation of a skin structure of a subject. The system
comprises a processor configured for performing steps of the
method. Steps of the method comprise: acquiring a plurality of
two-dimensional (2D) cross-sectional images of the skin structure;
computing a cost for each 2D cross-sectional image based on a cost
function, the cost function comprising an edge-based parameter and
a non-edge-based parameter; constructing a 3D graph from the 2D
cross-sectional images; and determining a minimum-cost closed set
from the 3D graph based on the computed costs for the 2D
cross-sectional images, wherein the 3D representation of the skin
structure is generated from the minimum-cost closed set.
[0025] An advantage of the present disclosure is that the 3D
representation of the skin structure enables for more accurate
analysis of the skin structure and surface, addressing one or more
of the aforementioned limitations of existing methods in segmenting
skin surface with hair. The 3D representation is formed by
constructing a 3D graph from multiple 2D cross-sectional images
such that information from neighbouring/adjacent images is
considered. Since a single instance of hair is usually present in
only a few consecutive images, collective consideration of all the
images attenuate the effects of hair presence in some of the
images, resulting in a more accurate representation of the skin
structure and surface. Such information is neglected in existing
surface methods because they are essentially 2D methods which are
not robust to the presence of hair. The present disclosure thus
provides for generation of a 3D representation of the skin
structure wherein there is better accuracy in the segmentation of
the skin structure, even in the presence of hair where existing 2D
methods fail. The 3D representation generated from the 3D graph
thus presents an improvement over existing 2D graph-based
methods.
[0026] An automated method and system for generating a 3D
representation of a skin structure of a subject according to the
present disclosure are thus disclosed herein. Various features,
aspects, and advantages of the present disclosure will become more
apparent from the following detailed description of the embodiments
of the present disclosure, by way of non-limiting examples only,
along with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] FIG. 1A illustrates different appearances of hair in various
OCT images of a skin structure.
[0028] FIG. 1B illustrates various images of skin segmentation
failures due to presence of hair.
[0029] FIG. 2A illustrates a skin surface affected by presence of
hair.
[0030] FIG. 2B illustrates a skin structure with a false skin
surface boundary due to presence of hair.
[0031] FIG. 3 illustrates an OCT volume having a plurality of 2D
cross-sectional images of a skin structure.
[0032] FIG. 4 illustrates a flowchart of a method for generating a
3D representation of a skin structure of a subject.
[0033] FIG. 5 illustrates a 2D cross-sectional image.
[0034] FIG. 6 illustrates various images for comparison of
smoothness of 3D representations of the skin structure.
[0035] FIG. 7 illustrates evaluation results for various 2D
cross-sectional images.
[0036] FIG. 8 illustrates a flowchart of a method for assessing
skin roughness of a subject.
[0037] FIG. 9A illustrates an example of a skin surface topographic
analysis for assessing skin roughness.
[0038] FIG. 9B illustrates another example of a skin surface
topographic analysis for assessing skin roughness.
[0039] FIG. 10 illustrates a sliding window approach in the skin
surface topographic analysis.
DETAILED DESCRIPTION
[0040] In the present disclosure, depiction of a given element or
consideration or use of a particular element number in a particular
figure or a reference thereto in corresponding descriptive material
can encompass the same, an equivalent, or an analogous element or
element number identified in another figure or descriptive material
associated therewith. The use of "/" in a figure or associated text
is understood to mean "and/or" unless otherwise indicated. As used
herein, the term "set" corresponds to or is defined as a non-empty
finite organization of elements that mathematically exhibits a
cardinality of at least one (e.g. a set as defined herein can
correspond to a unit, singlet, or single element set, or a multiple
element set), in accordance with known mathematical definitions.
The recitation of a particular numerical value or value range
herein is understood to include or be a recitation of an
approximate numerical value or value range.
[0041] For purposes of brevity and clarity, descriptions of
embodiments of the present disclosure are directed to an automated
method and system for generating a three-dimensional (3D)
representation of a skin structure of a subject, in accordance with
the drawings. While aspects of the present disclosure will be
described in conjunction with the embodiments provided herein, it
will be understood that they are not intended to limit the present
disclosure to these embodiments. On the contrary, the present
disclosure is intended to cover alternatives, modifications and
equivalents to the embodiments described herein, which are included
within the scope of the present disclosure as defined by the
appended claims. Furthermore, in the following detailed
description, specific details are set forth in order to provide a
thorough understanding of the present disclosure. However, it will
be recognized by an individual having ordinary skill in the art,
i.e. a skilled person, that the present disclosure may be practiced
without specific details, and/or with multiple details arising from
combinations of aspects of particular embodiments. In a number of
instances, well-known systems, methods, procedures, and components
have not been described in detail so as to not unnecessarily
obscure aspects of the embodiments of the present disclosure.
[0042] In representative or exemplary embodiments of the present
disclosure, there is a system including a processor, e.g. image
processing module/component, for performing an automated method for
generating a 3D representation of a skin structure of a subject.
Referring to FIG. 3, a plurality of two-dimensional (2D)
cross-sectional images 100 of the skin structure is acquired or
captured using an optical coherence tomography (OCT) technique. The
2D cross-sectional images 100 are captured as a series of B-scans
and may also be referred to as cross-sectional tomographs or
slices. The 2D cross-sectional images 100 collectively form an OCT
volume 110. As shown in FIG. 3, each 2D cross-sectional image or
slice 100 is along the x-z plane and the 2D cross-sectional images
in the OCT volume 110 stack along the y-direction.
[0043] FIG. 4 illustrates a flowchart of an automated method 200
performed by the processor for generating a 3D representation of
the skin structure. After the 2D cross-sectional images 100 in the
OCT volume 110 are acquired, each 2D cross-sectional image 100 is
subject to a preprocessing stage or phase 210 of the method 200.
The 2D cross-sectional images 100 may be captured using the OCT
technique in 16-bit resolution, e.g. in 16-bit grayscale. In the
preprocessing stage 210, each 2D cross-sectional image 100 is
converted to 8-bit grayscale. The contrast of each 2D
cross-sectional image 100 is then enhanced using contrast
stretching or normalization.
[0044] In one embodiment, in the preprocessing stage 210, 2D graphs
may be constructed from the plurality of 2D cross-sectional images
100 of the skin structure. A 2D graph represents or corresponds to
a 2D cross-sectional image 100. Each 2D graph includes a plurality
of nodes, each node corresponding to a pixel of the corresponding
2D cross-sectional image 100. The nodes are connected through
edges.
[0045] The method 200 further includes a cost computation stage or
phase 220 after the preprocessing stage 210. In the cost
computation stage 220, a cost for each 2D cross-sectional image 100
is computed based on a cost function, the cost function including
an edge-based parameter and a non-edge-based parameter. Details of
the cost function are elaborated below.
[0046] After the cost computation stage 220, a 3D graph 230 is
constructed from the 2D cross-sectional images 100. In another
embodiment, the 3D graph 230 may be constructed from the 2D graphs
which are in turn constructed from the 2D cross-sectional images.
The 3D graph 230 represents or corresponds to the OCT volume 110.
As with 2D graphs, the 3D graph 230 includes a plurality of nodes,
each node corresponding to a voxel of the OCT volume 110. The nodes
are connected through edges.
[0047] The method 200 further includes a subsequent step 240 of
determining a minimum-cost closed set from the 3D graph 230 based
on the computed costs for the 2D cross-sectional images 100. The
minimum-cost closed set is determined using the standard max-flow
min-cut theorem or algorithm [Reference 10] which would be readily
understood by the skilled person. An output skin structure and
surface 250 is generated from the minimum-cost closed set. This
output skin structure 250 is also the 3D representation 250 of the
skin structure. The 3D representation 250 includes a plurality of
voxels, each voxel corresponding to one of the nodes of the 3D
graph 230. Furthermore, the 3D representation 250 corresponds to
the desired skin structure of the subject which can be subsequently
analyzed to assess skin health and/or various skin conditions of
the subject.
[0048] In embodiments of the present disclosure, the following
notations and terms are used to describe various aspects of the
method 200. [0049] N.sub.x, N.sub.z: Size of 2D cross-sectional
image 100 along x and z directions, respectively. [0050] I(x, y,
z): 3D representation 250 of the skin structure representing the
OCT volume 110 with I(x, y, z) being the intensity at a voxel (x,
y, z). Here x ={1, 2, . . . N.sub.x} represents the voxel indices
along the x-axis. y and z are similarly defined. [0051] :
(x,y).fwdarw.(x,y): Function defining a surface where (x,y) .
[0052] G=(V, E): 3D graph 230 representing I. [0053] V(x, y, z):
Node in G corresponding to voxel (x, y, z). [0054] Column: Set of
nodes with same (x, y) values. In the context of a 2D
cross-sectional image 100, a column refers to all pixels with the
same value of x. [0055] Base layer: Set of all nodes corresponding
to z=0. [0056] C: A closed set C is the set of all those nodes such
that the successors of each node in C are also contained in C.
[0057] .DELTA..sub.x, .DELTA..sub.y: Constants determining
smoothness of the skin surface along x (within a 2D cross-sectional
image 100) and y (across adjacent 2D cross-sectional images 100)
directions, respectively. [0058] c(x, y, z): Cost function for a
voxel (x, y, z). [0059] c.sub.Y(x,z): Cost function for a 2D
cross-sectional image 100 corresponding to y=Y. [0060] w(x, y, z):
Weight assigned to the node V(x, y, z).
[0061] In the 3D graph 230 or G=(V, E), V denotes the nodes while E
denotes the edges. Each voxel in I has a corresponding node in the
3D graph 230. Each node V is connected only to its neighbouring
nodes through the edges E. In a 4-neighbour setting, V(x, y, z) is
connected to 4 neighbours outside its own column, namely (i) V(x-1,
y, z-.DELTA..sub.x), (ii) V(x+1, y, z-.DELTA..sub.x), (iii) V(x,
y-1, z-.DELTA..sub.y), and (iv) V(x, y+1, z-.DELTA..sub.y). In
addition, each node (except for nodes in the base layer) is also
connected to the node just below it, i.e. V(x, y, z) is connected
to V(x, y, z-1). These edges enforce the smoothness constraints.
This means that for a voxel (x, y, z) on a surface , its
neighbouring voxels along the x-direction, i.e. (x+1, y, z') and
(x-1, y, z''), are not lower than the voxel (x, y, max(0,
z-.DELTA..sub.x)).
[0062] A cost can be computed for each node in the cost computation
stage 220. The choice of the cost function used in the cost
computation stage 220 is crucial for accurate generation of the 3D
representation 250 of the skin structure. In order to find the
minimum closed set, cost function has to be chosen such that it has
a low value for voxels on the desired skin structure and a high
value elsewhere. Usually, the appearance of the surface of the skin
structure in a 2D cross-sectional image 100 is characterized by a
surface profile with prominent edges. This characteristic can be
captured by using the edge-based parameter in the cost function.
However, for skin structures, there are other layers which can have
strong gradients near their boundaries. To distinguish these layers
from the skin surface, the cost function also includes a
non-edge-based parameter.
[0063] FIG. 5 illustrates a 2D cross-sectional image 100 with the
surface of the skin structure characterized by the surface profile.
FIG. 5 further illustrates a 2D cross-sectional image 100 having a
plurality of nodes 260 and a plurality of edges 270. A weight value
is assigned to each edge 270 to represent the cost to path through
the edge. To travel between the nodes 260, the total cost is the
sum of all weight values assigned to the edges 270 connecting the
nodes 260. Due to the presence of hairs which may attract the
pathing and lead to false detection of the skin surface profile or
boundary, the edges 270 have to be assigned with appropriate weight
values.
[0064] A high cost is assigned to non-edge nodes/pixels. A mask
M(x, y) is computed as follows:
M ( x , y ) { 1 0 .ltoreq. grad ( x , y ) < T 0 otherwise [ 1 ]
##EQU00001##
[0065] The weight v(x, y) for an edge 270 between two nodes 260 is
calculated based on the gradients at the nodes 260 as follows:
v(x,y)=2-grad(x,y)-grad(x.sub.n,y.sub.n)+.lamda.M(x,y)+.epsilon.
[2]
where grad(x, y) is the vertical gradient of the 2D cross-sectional
image 100 at (x, y), grad(x.sub.n, y.sub.n) is the vertical
gradient of the 2D cross-sectional image 100 at node (x.sub.n,
y.sub.n), .lamda. is a tuning parameter controlling the weight of
the mask M(x, y), and .epsilon.=10.sup.-5 is the minimum weight in
the 2D cross-sectional image 100 added for stabilization. T may be
set at 3 as the performance has been empirically shown to be
insensitive to very small T ranging from 1 to 5. .lamda. may be set
at 100, which is also empirically shown to be insensitive to the
performance.
[0066] Equation [2] assigns low weight values to node pairs with
large vertical gradients. In one example implementation, the
gradients are normalized to values between 0 and 1. These weights
are further adjusted to account for the directionality of the
gradient. In some cases, if it is known that a boundary of the skin
structure exhibits a dark to bright transition, only the dark to
bright gradient is computed. After assigning the weight values,
graph search algorithms such as the max-flow min-cut algorithm are
used to determine the minimum path that connects the two
endpoints.
[0067] The cost is thus computed for all nodes 260 of the 2D
cross-sectional images 100. The 3D graph 230 is constructed from
the 2D cross-sectional image 100. Notably, the cost for each node
260 corresponds to the cost for a pixel of the corresponding 2D
cross-sectional image 100 and further corresponds to the cost for a
voxel of the OCT volume 110.
[0068] The cost function c.sub.Y(x,z) for a 2D cross-sectional
image 100 (along the x-z plane) can be defined as:
c.sub.Y(x,z)=.rho.c.sub.Y.sup.edge(x,z)+(1-.rho.)c.sub.Y.sup.non(x,z)
[3]
where c.sub.Y.sup.edge and c.sub.Y.sup.non are the edge-based and
non-edge-based parameters, respectively. The parameter .rho.
controls the weightage given to the edge-based and non-edge-based
parameters. The edge-based parameter is associated with the
gradient information while non-edge-based parameter is associated
with other information such as the homogeneity of the imaged
portions above and below the skin surface.
[0069] The cost function thus has a combination of edge-based and
non-edge-based parameters. When the edge-based parameter is
included to the cost function for eventually generating the 3D
representation 250 of the skin structure, the cost function gave a
low cost to both the surface of the skin structure and another
layer below it which also has an edge combined with a dark to
bright transition. To avoid any confusion with the layers below the
skin surface, the cost function includes the non-edge-based
parameter to cooperate together with the edge-based parameter. This
allows the skin surface to be clearly distinguished from the layers
below it.
[0070] The edge-based and non-edge-based parameters are more
specifically defined as follows:
c.sub.Y.sup.edge(x,z)=-e.sub.Y(x,z)p(.PHI..sub.Y(x,z)) [4]
c.sub.Ynon(x,z)=b.sub.Y(x,z)+r.sub.Y(x,z) [5]
The term p(.PHI..sub.Y(x,z)) is an orientation penalty the term
.PHI..sub.Y(x,z) is a gradient orientation. The orientation penalty
p(.PHI..sub.Y(x,z)) is a function of the gradient orientation
.PHI..sub.Y(x,z) and is defined as:
p ( .phi. Y ( x , z ) ) = { 0 , if .phi. Y ( x , z ) = 270 .degree.
1 , if 0 .degree. .ltoreq. .phi. Y ( x , z ) .ltoreq. 180 .degree.
< 1 otherwise [ 6 ] ##EQU00002##
[0071] The orientation penalty p(.PHI..sub.Y(x,z)) is defined in
view of the dark to bright transition at the skin surface when
traversing from top to bottom of the 2D cross-sectional image 100
(z is decreasing). At this dark to bright transition, the gradient
orientation .PHI..sub.Y(x,z) is expected to vary from 0 to 180 so
p(.PHI..sub.Y(x,z))=1 for such cases. Conversely, for bright to
dark transitions, cost should be high which is achieved by
assigning the orientation penalty p(.PHI..sub.Y(x,z)) a value less
than 1. Values of p(.PHI..sub.Y(x,z))<1 are computed by linear
interpolation.
[0072] The term e.sub.Y(x,z) in Equation [4] is a thresholding
function which suppresses all the nodes/pixels where the first and
second image derivatives are below a threshold. e.sub.Y(x,z) is
defined as:
e Y ( x , z ) = { p Y min , if 1 Y 2 D ( x , z ) < .theta. 1 Y (
x , z ) and 2 Y 2 D ( x , z ) < .theta. 2 Y ( x , z ) 1 ,
otherwise [ 7 ] ##EQU00003##
where p.sub.Y.sup.min=min(p(.PHI..sub.Y(x,z))) (.gtoreq.0 from
Equation [6]), while .theta..sub.1Y(x,z) and .theta..sub.2Y(x,z)
are adaptive thresholds determined from the gradient information
and the values are computed separately for each 2D cross-sectional
image 100. .theta..sub.1Y(x,z) for a 2D cross-sectional image 100
may be defined such that 95% of the pixels have the absolute value
of the first image derivative, .sub.1Y.sup.2D, to be less than
.theta..sub.1Y(x,z). Similarly, .theta..sub.2Y(x,z) may be defined
using the second image derivative, .sub.2Y.sup.2D. The image
derivatives may be computed by first blurring the image with
Gaussian kernel of size 3.times.3 followed by convolution with the
Scharr operator.
[0073] The edge-based parameter c.sub.Y.sup.edge uses the gradient
information to find points with higher gradients. The cost function
of existing methods finds points just above the surface with lower
cost. As a result, the skin surface has minor peaks. The
thresholding function e.sub.Y(x,z) in Equation [7] assigns a higher
cost to these points since gradients there are smaller.
Consequently, these minor peaks are removed.
[0074] The term b.sub.Y(x,z) in Equation [5] is a measure of the
number of bright pixels above each pixel. This term is used to
differentiate between a dark to bright transition at the skin
surface from those at other locations below the skin surface,
thereby helping to overcome false segmentation at the lower layers
below the skin surface which could result in inaccurate definition
of the skin surface.
[0075] The value of b.sub.Y(x,z) at the skin surface is lower than
its value below the skin surface because the portion above the skin
surface is all dark. The values of b.sub.Y(x,z) is also decreasing
from the skin surface to the lower layers of the skin structure. In
one example computation of b.sub.Y(x,z), a bright pixel is defined
as any pixel with an intensity greater than a threshold, which may
be empirically defined as 200.
[0076] The term r.sub.Y(x,z) refers to the 2D version of the
non-edge-based parameter of the cost function, and is equal to the
sum of the inside and outside variances computed for a column of a
2D cross-sectional image 100. The term r.sub.Y(x,z) is defined for
a pixel (x',Y,z') as:
r Y ( x ' , z ' ) = z .ltoreq. z ' ( I ( x ' , Y , z ) - a 1 2 ) +
z > z ' ( I ( x ' , Y , z ) - a 2 2 ) [ 8 ] ##EQU00004##
where a.sub.1 and a.sub.2 are constants which can be approximated
with a.sub.1(x',Y,z') and a.sub.2(x',Y,z').
[0077] a.sub.1(x',Y,z') and a.sub.2(x',Y,z') are defined for each
pixel/node in the 2D cross-sectional image 100 (at y=Y) as
follows:
a.sub.1(x',Y,z')=mean(I(x,Y,z.sub.1)) [9]
a.sub.2(x',Y,z')=mean(I(x,Y,z.sub.2)) [10]
where .sub.1.ident.{z|z.ltoreq.max(0,z'-|x-x'.DELTA..sub.x)} and
.sub.2.ident.{z|z'+|x-x'.DELTA..sub.x<z<N.sub.z}. Both
b.sub.Y(x,z) and r.sub.Y(x,z) are normalized such that their values
reside in the same range as that of c.sub.Y.sup.edge(x,z).
[0078] The cost computation stage 220 thus computes the cost for
each pixel of the 2D cross-sectional images 100 and this
corresponds to the cost of the voxel at that location. The 3D
representation 250 of the skin structure is generated based on the
cost assigned to each voxel. The cost of the surface of the skin
structure is computed as the sum of the costs of all the voxels
lying on it. The step 240 of the method 200 finds the skin surface
with the minimum cost by determining the closed set C with the
minimum cost, i.e. the minimum-cost closed set, from the 3D graph
230 based on the computed costs.
[0079] The cost of the closed set C is the sum of the costs of the
nodes 260 contained in the closed set C. This equivalence is
achieved by defining the weight of a node V (x, y, z) in the 3D
graph 230 or G=(V, E) as:
w ( x , y , z ) = { c ( x , y , z ) , if x = 0 c ( x , y , z ) - c
( x , y , z - 1 ) , otherwise [ 11 ] ##EQU00005##
[0080] Once weights are defined at the nodes 260 of the 3D graph
230, the 3D graph 230 becomes a node-weighted directed graph
(digraph). As described above in the step 240, the minimum-cost
closed set in this digraph can be found using the standard max-flow
min-cut algorithm and this set corresponds to the 3D representation
250 of the skin structure or the desired skin structure of the
subject.
[0081] For evaluation of the effectiveness of the 3D
representations 250 generated by the method 200, datasets are
constructed for two types of 2D cross-sectional images or slices
100, namely those with hair and those without hair. The first
dataset (skin dataset 1 or SD1) consists of 5 OCT volumes 110
without hair. The OCT volumes 110 in SD1 are captured from the
palms and soles of subjects or patients at a medical facility or
hospital. The second dataset (skin dataset 2 or SD2) consists of
252 2D cross-sectional images 100 extracted from 26 OCT volumes 110
with hair. The OCT volumes 110 in SD2 are captured from the
anterior and posterior forearms and the face of the
subjects/patients. The 252 2D cross-sectional images 100 are
selected through visual inspection of the 26 OCT volumes 110. Only
2D cross-sectional images 100 where hair is visibly prominent are
selected. In addition, if there are consecutive or adjacent 2D
cross-sectional images 100 which appear similar to each other, only
one of these 2D cross-sectional images 100 are selected to avoid
compromising the dataset SD2 due to similar-looking images. Each 2D
cross-sectional image 100 may be of the size 360.times.200 pixels.
Ground truth, i.e. information from direct observation, for the
surface of the skin structure is manually marked for all the 2D
cross-sectional images 100 in the datasets SD1 and SD2.
[0082] In the generated 3D representation 250 of the skin
structure, the intersection of the skin surface with each 2D
cross-sectional image 100 has a curvilinear profile. An evaluation
of the accuracy of the 3D representation 250 is performed for each
2D cross-sectional image 100. The evaluation includes experiments
performed on both the datasets SD1 and SD2. The curvilinear profile
for each 2D cross-sectional image 100 is compared to the ground
truth. The evaluation metric for a 2D cross-sectional image 100 is
the unsigned mean vertical error which is given as:
E u = 1 N x ( x = 1 N x z x pred - z x GT ) [ 12 ] ##EQU00006##
where z.sub.x.sup.pred is the z coordinate for the x.sup.th column
derived from the curvilinear profile, and z.sub.x.sup.GT is the
actual z coordinate for the x.sup.th column derived from the ground
truth or direct observation.
[0083] The evaluation results for the method 200 are compared with
those for existing methods and are shown in Table 1 below. The
values in Table 1 refer to the unsigned mean vertical error. The
values in braces are the standard deviations and the best results
are underlined. The constants determining smoothness of the skin
surface are .DELTA..sub.x=2 and .DELTA..sub.y=3 for all the
experiments.
TABLE-US-00001 TABLE 1 SD1 (5 OCT Volumes) 2nd SD2 1st OCT OCT 3rd
OCT 4th OCT 5th OCT (252 Volume Volume Volume Volume Volume Mean
Images) First 2.12 1.38 2.04 1.68 1.99 1.84 3.97 existing (0.35)
(0.28) (0.32) (0.54) (0.36) (6.45) method Second 1.82 1.21 2.15
1.56 1.49 1.65 3.29 existing (0.90) (0.27) (1.95) (0.94) (0.29)
(5.72) method Method 1.75 1.53 1.67 1.80 1.41 1.63 1.97 200 (0.32)
(0.32) (0.37) (0.58) (0.27) (0.69)
[0084] The first existing method refers to a 2D graph-based method
for skin surface segmentation whereby vertical intensity gradients
are used in the cost function. The second existing method refers to
a 2D graph-based method whereby vertical intensity gradients and
gradient magnitude are used in the cost function.
[0085] From Table 1, it can be seen that for the dataset SD1, the
performance of the 3D representation 250 from the method 200 is
almost same as that of the existing methods. This is because
introduction of three dimensions only adds an additional connection
(in the OCT volume 110) across the 2D cross-sectional images 100,
which gives information from neighbouring/adjacent 2D
cross-sectional images 100. This information may not be necessary
if there is no or minimal presence of hair on the skin surface of
the subjects, as is the case for the dataset SD1. The existing
methods are able to provide accurate segmentation of the skin
structure when there is no hair but this information adds
smoothness to the results as illustrated in FIG. 6. Thus, accuracy
may not be higher for such cases without hair on the skin
surface.
[0086] Referring to FIG. 6, image (i) illustrates the 3D
representation 250 of the skin structure and surface derived from
an OCT volume 110 according to the method 200. Image (ii)
illustrates the 3D representation 250 when viewed in the direction
of the arrow shown in image (i). Image (iii) illustrates a
segmentation of the same OCT volume 110 using an existing 2D
method. As seen from FIG. 6, the skin surface of the 3D
representation 250 from the method 200 is smoother than that from
the existing 2D method, evidently due to the presence of artifacts
on the skin surface in image (iii).
[0087] For the dataset SD2 wherein there is presence of hair, the
effectiveness or advantage of the 3D representation 250 from the
method 200 is more apparent and evident by the values shown in
Table 1. Due to connections across the 2D cross-sectional images
100, the 3D representation 250 improves smoothness of the skin
surface. Even in the presence of hair, the skin surface smoothness
is maintained because the hair is not present in all of the 2D
cross-sectional images 100. In other words, for some of the 2D
cross-sectional images 100, the hair does not touch the skin
surface. When the 2D cross-sectional images 100 are considered
collectively against one another, the 3D representation 250 becomes
less affected by the presence of hair. The effectiveness of the 3D
representation 250 is evident by the evaluation results shown in
Table 1 as well as the illustrations in FIG. 7.
[0088] FIG. 7 illustrates evaluation results for four sample 2D
cross-sectional images 100. The top row of images is derived from
the first existing method. The middle row of images is derived from
the second existing method. The bottom row of images is derived
from the method 200. The unsigned mean vertical errors for the
samples are shown in Table 2 below.
TABLE-US-00002 TABLE 2 Sample (i) Sample (ii) Sample (iii) Sample
(iv) First existing 2.03 4.81 15.96 3.38 method Second 1.00 4.09
10.96 3.24 existing method Method 200 0.97 2.28 2.63 2.38
[0089] As evident from FIG. 7 and Table 2, the method 200
outperformed the existing methods as the unsigned mean vertical
errors are the lowest. Consequently, the reduction in error is more
than 39% as compared to the existing methods (Table 1).
[0090] The 3D representation 250 of the skin structure generated by
the method 200 thus enables for more accurate analysis of the skin
structure and surface. The 3D representation 250 is formed from
multiple 2D cross-sectional images 110 such that information from
neighbouring/adjacent images 100 is considered. Since a single
instance of hair is usually present in only a few consecutive
images 100, collective consideration of all the images 100
attenuate the effects of hair presence in some of the images 100,
resulting in a more accurate representation of the skin structure
and surface.
[0091] The method 200 and the generated 3D representation 250 of
the skin structure can be applied for assessment of skin
conditions. For example, in the cosmetics, pharmaceuticals, and/or
cosmeceuticals areas, the method 200 can be used as a potential
tool for assessing the efficacy of products for skin smoothing. The
method 200 may be performed by the system, e.g. including a skin
OCT machine, to provide analytical functionality to acquired OCT
volumes 110.
[0092] In some embodiments, there is provided an automatic skin
surface topographic analysis system for dermatological and
cosmeceutical practice (ASHIGA). The ASHIGA system automatically
analyzes high-definition OCT (HD-OCT) images, i.e. the 2D
cross-sectional images 100 of the skin structures of subjects, and
evaluates the skin surface topography using a set of roughness
parameters. Computation of the roughness parameters provides
dermatologists/clinicians an immediate and objective assessment of
a subject's skin roughness.
[0093] With reference to FIG. 8, there is a method 300 performed by
the ASHIGA system for assessing skin roughness of a subject based
on the 3D representation 250. The method 300 includes a step 310 of
capturing, by an OCT apparatus 20, the 2D cross-sectional images
100 of the skin structure and surface of the subject. The method
300 further includes a step 320 of identifying the skin surface
profile. The step 320 includes performing the method 200 to
generate a 3D representation 250 of the skin structure and surface.
The step 320 further includes a sub-step 322 of detecting the skin
surface profile from the 3D representation 250. Optionally, the
step 320 may include a sub-step 324 of removing hair from the skin
surface of the subject beforehand. However, this sub-step 324 is
unnecessary because, as described above, the presence of hair does
not adversely affect or compromise the results of further analysis
on the skin surface. Moreover, presence of hair on the skin surface
can be digitally removed by segmenting the skin surface profile of
the 3D representation 250, enabling the ASHIGA system to provide
for more accurate analyses.
[0094] The method 300 further includes a step 330 of performing
skin surface topographic analysis on the skin surface detected from
the 3D representation 250. In a subsequent step 340, values for a
set of roughness parameters are computed. The method 300 thus
automatically processes the 2D cross-sectional images 100, evaluate
the roughness of the skin surface detected from the 3D
representation 250, and compute the values for the set of roughness
parameters for evaluation of the skin surface roughness.
[0095] In one embodiment with reference to FIG. 9A, there is a skin
surface topographic analysis 400A performed in in the step 330 of
the method 300. In another embodiment with reference to FIG. 9B,
there is a skin surface topographic analysis 400B performed in in
the step 330 of the method 300. Each of FIGS. 9A and 9B illustrates
the process flow of the skin surface topographic analyses 400A and
400B, respectively, to analyze the skin surface topography for
computing the roughness parameters in the step 340.
[0096] The skin surface topographic analysis 400A/400B includes a
step 410 of identifying or detecting the skin surface topography
412 from the 3D representation 250 of the skin structure. Notably,
the step 410 is equivalent of the sub-step 322 described above. As
the various areas of the skin surface topography 412 are not on the
same plane, the skin surface topographic analysis 400A/400B
includes a plane rectification process 420 to correct the planes
for the various areas of the skin surface topography 412 to a
common plane, thereby generating a plane-rectified skin surface
topography 422. The skin surface topographic analysis 400A/400B
further includes a depth map generation process 430 for generating
a 2D depth map 432 based on the plane-rectified skin surface
topography 422.
[0097] The 2D cross-sectional images 100 are captured by an imaging
probe of the OCT apparatus 20 that produces en face images of size
640.times.512. However, there is a noticeable signal falloff at the
edges of the imaging field of view. It is empirically observed that
the signal within a circular region is of better quality compared
to the signal outside the circular region. Hence, the size of the
2D depth map is defined as 360.times.360, which is the maximum
inscribed square to avoid noticeable signal falloff.
[0098] In the skin surface topographic analysis 400B as shown in
FIG. 9B, the 2D depth map 432 may be subject to an additional 2D
median filter process 440. A filtered 2D depth map 442 is
consequently generated. The 2D median filter process 440 may be
performed on the unfiltered 2D depth map 432 to remove noise
therefrom.
[0099] In a step 450, the roughness parameters are computed based
on the 2D depth map 432. It will be appreciated that, in another
embodiment, the roughness parameters may be computed based on the
filtered 2D depth map 442. It will also be appreciated that the
step 450 is equivalent to the step 340 of the method 300.
[0100] Skin surface topography can be described with the roughness
parameters defined by the International Organization for
Standardization (ISO) in ISO 4287-1977. The roughness parameters
can be catergorized into two types--amplitude and frequency
parameters. In the skin surface topography analysis 400A/400B to
assess the roughness of the skin surface, five amplitude parameters
R.sub.a, R.sub.q, R.sub.t, R.sub.zISO, and R.sub.max are used as
the roughness parameters.
[0101] As shown in FIG. 10, a sliding window approach on the 2D
depth map 432 is used to compute/calculate the roughness
parameters. The sliding window 452 is set to a size of n.times.n,
where the reference point, p is always at the centre of the sliding
window 452. The sliding window 452 is moved across a plurality of
discrete steps 454 over the 2D depth map 432 until the entire area
of the 2D depth map 432 has been scanned by the sliding window 452.
It will be appreciated that the discrete steps 454 may be defined
such that there is an overlap between every pair of consecutive
sliding windows 452.
[0102] For each discrete step 454 wherein the sliding window 452 is
placed over a window area 456 of the 2D depth map 432, the five
roughness parameters are computed and the computed results define
the local (said window area 456 of the 2D depth map 432) values of
of R.sub.a, R.sub.q, R.sub.t, R.sub.zISO, and R.sub.max at the
centre p.
[0103] The first roughness parameter R.sub.a refers to the average
deviation of depth profile over each window area 456, and is
defined as follows:
R a = 1 N n = 1 N r n [ 13 ] ##EQU00007##
where N is the number of pixels in the window area 456 and r.sub.n
is the depth value at each pixel.
[0104] The second roughness parameter R.sub.q refers to the
root-mean-square average roughness in the window area 456, and is
defined as follows:
R q = 1 N n = 1 N r n 2 [ 14 ] ##EQU00008##
[0105] The third roughness parameter R.sub.t refers to the sum of
the maximum depth value and minimum depth value in the window area
456, and is defined as follows:
R.sub.t=|max [r.sub.n]|+|min [r.sub.n]| [15]
The fourth roughness parameter R.sub.zISO is a parameter that
averages the sum of five highest peaks height and five deepest
valleys depth over the evaluation length. R.sub.zISO is defined as
follows:
R zISO = 1 5 ( n = 1 5 max [ r n ] + n = 1 5 min [ r n ] ) [ 16 ]
##EQU00009##
[0106] The fifth roughness parameter R.sub.max is a parameter which
serves a purpose similar to R.sub.t. R.sub.max determines the
extreme peak-to-valley length from five sampling lengths. Within
the window area 456, the window area 456 is sub-divided into
smaller regions or patches. The sliding window 452 may be similarly
sub-divided into the same number of smaller regions or patches 458
as shown in FIG. 10. Each region 458 may have a size of
n/5.times.n/5, and R.sub.t is computed for each patch 458. The
highest value of R.sub.t among all the regions 458 is defined as
the R.sub.max for the window area 456.
[0107] Experiments are performed on various sets of test images for
assessing skin roughness. The calculated roughness parameters for
different window sizes n.times.n are shown in Tables 3A to 3C
below. The experimental results show that the roughness parameters
are insensitive to window sizes of n=20, n=30, and n=40.
TABLE-US-00003 TABLE 3A Mean Test Subjective n .times. n = 20
.times. 20 Images Ranking R.sub.a R.sub.q R.sub.t R.sub.zISO
R.sub.max Set 1 IMG01 3 2.72 3.29 14.30 13.52 4.61 IMG02 2 1.11
1.37 6.55 6.05 2.83 IMG03 1 0.80 1.02 5.17 4.67 2.40 Set 2 IMG04
1.3 0.95 1.18 5.80 5.32 2.59 IMG05 2.4 2.82 3.40 14.90 14.05 5.12
IMG06 2.3 1.97 2.38 10.57 9.92 3.68 Set 3 IMG07 3 2.81 3.42 15.36
14.45 5.31 IMG08 1.75 2.40 2.91 13.21 12.34 4.71 IMG09 1.25 2.06
2.52 11.31 10.60 3.66
TABLE-US-00004 TABLE 3B Mean Test Subjective n .times. n = 30
.times. 30 Images Ranking R.sub.a R.sub.q R.sub.t R.sub.zISO
R.sub.max Set 1 IMG01 3 3.59 4.32 19.24 18.49 6.69 IMG02 2 1.31
1.63 8.42 7.91 3.78 IMG03 1 0.93 1.18 6.63 6.10 3.13 Set 2 IMG04
1.3 1.13 1.41 7.46 6.98 3.39 IMG05 2.4 3.71 4.46 19.85 19.05 7.32
IMG06 2.3 2.56 3.09 14.12 13.49 5.19 Set 3 IMG07 3 3.68 4.47 20.76
19.88 7.71 IMG08 1.75 3.14 3.79 17.58 16.75 6.69 IMG09 1.25 2.67
3.25 14.98 14.31 5.38
TABLE-US-00005 TABLE 3C Mean Test Subjective n .times. n = 40
.times. 40 Images Ranking R.sub.a R.sub.q R.sub.t R.sub.zISO
R.sub.max Set 1 IMG01 3 4.33 5.21 23.41 22.68 8.63 IMG02 2 1.49
1.86 10.12 9.59 4.60 IMG03 1 1.05 1.33 7.92 7.37 3.77 Set 2 IMG04
1.3 1.30 1.62 9.01 8.51 4.08 IMG05 2.4 4.47 5.37 24.04 23.25 9.40
IMG06 2.3 3.06 3.70 17.27 16.65 6.61 Set 3 IMG07 3 4.43 5.38 25.42
14.45 5.31 IMG08 1.75 3.78 4.56 21.30 12.34 4.71 IMG09 1.25 3.16
3.84 18.04 10.60 3.66
[0108] Existing skin surface topography evaluation techniques such
as replica-based methods have limitations, which may result in
errors and inaccurate measurements. The ASHIGA system described in
the method 300 provides an objective skin surface topography
assessment tool using the 2D cross-sectional images 100, e.g.
HD-OCT images acquired by the OCT apparatus 20, which is a type of
non-invasive skin image modality used clinically. The ASHIGA system
can also be embedded in skin OCT manufacturers to provide
analytical functionality to acquired raw OCT or HD-OCT images.
[0109] The ASHIGA system may be used in skin and dermatology
clinics for assessment of skin conditions, e.g. roughness. The
ASHIGA system may also be a potential tool for the cosmetics,
pharmaceuticals, and/or cosmeceuticals fields to assess efficacy of
products for skin smoothing.
[0110] Some of the advantages of the ASHIGA system over the
existing methods include, but are not limited to, the following:
[0111] Automated system that enables objective and fast assessment
of skin characteristics, e.g. roughness. [0112] Enables on-site
assessment of skin roughness for immediate feedback to
dermatologists/clinicians and subjects/patients. [0113] Independent
of lighting conditions and other external/environmental factors
which the existing methods are more sensitive to, such as body
movements and distortions of replicas. [0114] Independent
quantification of skin roughness that is consistent with findings
from dermatologists/clinicians and observers. [0115] Operates on
single set of scan data; does not require additional scans or
procedures unlike the skin replica assessment technique.
[0116] In the foregoing detailed description, embodiments of the
present disclosure in relation to an automated method and system
for generating a 3D representation of a skin structure of a subject
are described with reference to the provided figures. The
description of the various embodiments herein is not intended to
call out or be limited only to specific or particular
representations of the present disclosure, but merely to illustrate
non-limiting examples of the present disclosure. The present
disclosure serves to address at least one of the mentioned problems
and issues associated with the prior art. Although only some
embodiments of the present disclosure are disclosed herein, it will
be apparent to a person having ordinary skill in the art in view of
this disclosure that a variety of changes and/or modifications can
be made to the disclosed embodiments without departing from the
scope of the present disclosure. Therefore, the scope of the
disclosure as well as the scope of the following claims is not
limited to embodiments described herein.
* * * * *